This book will cover the fundamentals of neuromorphic engineering, focusing on the digital neuron model for neuro-synaptic cores, cognitive programming paradigms and algorithms, neuromorphic circuits and applications for networks of neuro-synaptic cores.
This book will cover the fundamentals of neuromorphic engineering, focusing on the digital neuron model for neuro-synaptic cores, cognitive programming paradigms and algorithms, neuromorphic circuits and applications for networks of neuro-synaptic cores.
Dr. Elishai Ezra Tsur is the principal investigator at the Neuro & Biomorphic Engineering Lab (NBEL-lab.com) and an Assistant Professor at the Open University of Israel. Elishai Holds degrees in Life sciences (B.Sc), Philosophy and History (B.A), Computer Science (M.Sc), Bioengineering (M.Sc, Ph.D.), and Computational Neuroscience (Post Doc).
Inhaltsangabe
Part 1. Introduction and overview. 1. The Scientist Perspective. 2. Introducing the perspective of the computer architect. 3. Introducing the perspective of the algorithm designer. Part 2. The Scientist's Perspective. 4. Biological description of neuronal dynamic. 5. Models of point neuronal dynamic. 6. Models of morphologically detailed neurons. 7. Models of network dynamic and learning. Part 3. The Computer Architect's Perspective. 8. Neuromorphic hardware. 9. Communication and hybrid circuit design. 10. In-memory computing with memristors. Part 4. The Algorithms Designer's Perspective. 11. Introduction to neuromorphic programming. 12. The Neural Engineering Framework (NEF). 13. Learning spiking neural networks.
Part 1. Introduction and overview. 1. The Scientist Perspective. 2. Introducing the perspective of the computer architect. 3. Introducing the perspective of the algorithm designer. Part 2. The Scientist's Perspective. 4. Biological description of neuronal dynamic. 5. Models of point neuronal dynamic. 6. Models of morphologically detailed neurons. 7. Models of network dynamic and learning. Part 3. The Computer Architect's Perspective. 8. Neuromorphic hardware. 9. Communication and hybrid circuit design. 10. In-memory computing with memristors. Part 4. The Algorithms Designer's Perspective. 11. Introduction to neuromorphic programming. 12. The Neural Engineering Framework (NEF). 13. Learning spiking neural networks.
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